University of Twente Student Theses


Smart sports exercises project : the detection of (in)correct motion during resistance training

Jeulink, Kevin (2020) Smart sports exercises project : the detection of (in)correct motion during resistance training.

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Abstract:In this research a framework in Matlab is developed, which identifies the incorrect body motions of a squat using inertial measurement units. Regarding this development, certain research questions arises. First, what are the quality metrics of resistance training? Secondly, to what extent can IMUs detect those metrics, and what is the quality of this detection? Two personal trainers and a sport physiotherapist are interviewed, to determine which exercises are important and have a high chance on injuries. Based on the outcome of the interviews, literature is used to identify the quality metrics of the most common exercise, the squat. Those metrics are used to design the process chain and output of the framework. The algorithm uses the Euler angles of the limbs as input. The end application is able to detect the timing of a squat, the angle made by the trunk and the angle made by the knee joints. Optical measurements, using the software Kinovea, are done to validate the algorithm. Based on the validation the algorithm results deviate significantly from the optical measurements. The timing algorithm has an error of maximum 0.2 seconds. Moreover the obtained angles have a maximum error of 22 degrees. In the recommendation section, it is proposed to use acceleration data for decreasing the timing error. To decrease the error of the angles, it is suggested to use quaternions instead of Euler angles.
Item Type:Essay (Bachelor)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:53 electrotechnology, 54 computer science
Programme:Electrical Engineering BSc (56953)
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